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Computational intelligence with applications to general insurance: a review

II. Dealing with uncertain knowledge

Published online by Cambridge University Press:  22 May 2012

Pietro Parodi
Pietro Parodi*
Affiliation:
Willis Global Solutions (Consulting Group), London
*
*Correspondence to: Pietro Parodi, Willis Global Solutions (Consulting Group), Willis Ltd, 51 Lime Street, London EC3 M 7DQ. E-mail: [email protected]
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Abstract

This paper argues that most of the problems that actuaries have to deal with in the context of non-life insurance can be usefully cast in the framework of computational intelligence (a.k.a. artificial intelligence), the discipline that studies the design of agents which exhibit intelligent behaviour. Finding an adequate framework for actuarial problems has more than a simply theoretical interest: it also allows a technological transfer from the computational intelligence discipline to general insurance, wherever techniques have been developed for problems which are common to both contexts. This has already happened in the past (neural networks, clustering, data mining have all found applications to general insurance) but not in a systematic way. One of the objectives of this paper will therefore be to introduce some useful techniques such as sparsity-based regularisation and dynamic decision networks that are not yet known to the wider actuarial community.

Whilst in the first part of this paper we dealt mainly with data-driven loss modelling under the assumption that all the data were accurate and fully relevant to the exercise, in this second part of the paper we explore how to deal with uncertain knowledge, whether this uncertainty comes from the fact that the data are not fully reliable (e.g. they are estimates) or from the fact that the knowledge is “soft” (e.g. expert beliefs) or not fully relevant (e.g. market information on a given risk). Most importantly, we will deal with the problem of making pricing, reserving and capital decisions under uncertainty. It will be concluded that a Bayesian framework is the most adequate for dealing with uncertainty, and we will present a number of computational intelligence techniques to do this in practice.

Keywords

Intelligent agentsRisk agentsRegularisationMarkov decision processesGame theoryBayesian networksDynamic decision networksFuzzy set theoryExpert systemsNon-monotonic reasoningKalman filtering
Type
Papers
Information
Annals of Actuarial Science , Volume 6 , Issue 2 , 23 August 2012 , pp. 344 - 380
Copyright
Copyright © Institute and Faculty of Actuaries 2012

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References

Ambrosino, D., Fragnelli, V., Marina, M. (2006). Resolving an insurance allocation problem: A procedural approach. Social Choice and Welfare, 26(3), 625–643.CrossRefGoogle Scholar
Axelrod, R. (1984). The evolution of cooperation. Basic Books.Google Scholar
Bender, E.A. (1996). Mathematical Methods in Artificial Intelligence. IEEE Computer Society.Google Scholar
Bishop, C.M. (2007). Pattern Recognition and Machine Learning. Information Science and Statistics.Google Scholar
Cummins, J.D., Derrig, R.A. (1997). Fuzzy financial pricing of property-liability insurance. North American Actuarial Journal, 1(4), 21–44.CrossRefGoogle Scholar
Darley, V., Outkin, A., Plate, T., Gao, F. (2001). Learning, Evolution and Tick Size Effects in a Simulation of the Nasdaq Stock Market. Proceedings of the Joint 5th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2001) and the 7th International Conference on Information Systems Analysis and Synthesis (ISAS 2001).Google Scholar
De Jong, P., Zehnwirth, B. (1983). Claims reserving, state-space models and the kalman filter. Journal of the Institute of Actuaries, 110, 157–181.CrossRefGoogle Scholar
de Silva, C.J.S., Attikiouzel, Y. (1994). A better path to duplicating human reasoning. IEEE Expert: Intelligent Systems and Their Applications, 9(4), 14.Google Scholar
Elkan, C. (1994). The paradoxical success of fuzzy logic. IEEE Expert: Intelligent Systems and Their Applications, 9(4), 3–8.CrossRefGoogle Scholar
Fragnelli, V., Marina, M.E. (2003). A fair procedure in insurance. Insurance: Mathematics and Economics, 33(1), 75–85.Google Scholar
Ghahramani, Z., Hinton, G.E. (1998). Variational learning for switching state-space models. Neural Computation, 12(4), 963–996.Google Scholar
Hamilton, J.D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57, 357–384.CrossRefGoogle Scholar
Lemaire, J. (1990). Fuzzy insurance. ASTIN Bulletin, 20(1), 33–55.CrossRefGoogle Scholar
MacKay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms (4th Printing). Cambridge University Press.Google Scholar
Nikolaidis, E., Cudney, H.H., Chen, Q. (1999). Bayesian and fuzzy set methods for design under uncertainty. Virginia Polytechnic Institute and State University. 13th ASCE Engineering Mechanics Division Conference. Baltimore, Maryland.Google Scholar
Papadimitriou, C. (1994). Computational complexity. Addison-Wesley, Reading, Massachusetts.Google Scholar
Parodi, P. (2009). Computational intelligence techniques for general insurance. Research dissertation for the Actuarial Profession. Available at http://www.actuaries.org.uk/research-and-resources/documents/computational-intelligence-techniques-general-insuranceGoogle Scholar
Pearl, J. (1988). Probabilistic reasoning in intelligent systems : networks of plausible inference. Morgan Kaufmann Publishers, San Mateo, Calif., 1988.Google Scholar
Popper, K. (1959). The propensity interpretation of probability. The British Journal for the Philosophy of Science, X(37), 25–42.CrossRefGoogle Scholar
Roberts, C.P., Casella, G. (2004). Monte Carlo Statistical Methods (2nd Edition). Springer-Verlag, New York.CrossRefGoogle Scholar
Russell S.J., Norvig (2003). Artificial Intelligence: A Modern Approach (Second Edition). Prentice Hall.Google Scholar
Ribeiro, I. (2004). Kalman and Extended Kalman Filters: Concept, Derivation and Properties. Institute for Systems and Robotics, IST, (http://www.isr.ist.utl.pt/~mir)Google Scholar
Roberts, S. (2011). (http://www.robots.ox.ac.uk/~sjrob). Personal communication.Google Scholar
Semagul, A., Altindas, E., Macit, R., Umar, S., Unal, H. (2009). fuzzyOP: Fuzzy numbers and the main mathematical operations. R Package (http://cran.r-project.org/web/packages/fuzzyOP/index.html)Google Scholar
Shapiro, A.F. (2004). Fuzzy logic in insurance. Insurance: Mathematics and Economics, 35(2), 399–424.Google Scholar
Taylor, G. (2008). A simple model of insurance market dynamics. GIRO proceedings. Available at http://www.actuaries.org.uk/research-and-resources/documents/simple-model-insurance-market-dynamics-slidesCrossRefGoogle Scholar
Wooldridge, M.J. (2001). Multi-agent systems : an introduction. Wiley, Chichester GBA1-Z6596 Michael Wooldridge.Google Scholar
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar
Zarchan, P., Musoff, H. (2005). Fundamentals of Kalman Filtering: A Practical Approach (2nd ed). American Institute of Aeronautics and Astronautics.Google Scholar